[1] [1] Akula A, Ghosh R, Kumar S, et al. Moving target detection in thermal infrared imagery using spatiotemporal information \[J\]. Journal of the Optical Society of America A: Optics and Image Science and Vision, 2013, 30(8): 1492-1501.

[2] [2] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition \[J\]. arXiv: 1409.1556, 2014.

[3] [3] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition \[C\]. Las Vegas: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[4] [4] Howard A G, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications \[J\]. arXiv:170404861, 2017.

[5] [5] Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks \[J\]. arXiv: 1905.11946, 2019.

[6] [6] Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs \[J\]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.

[7] [7] Hao Y, Liu Y, Wu Z, et al. Edgeflow: achieving practical interactive segmentation with edge-guided flow \[C\]. Montreal: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021.

[8] [8] Yu Y, Yang Y, Liu K. Edge-aware superpixel segmentation with unsupervised convolutional neural networks \[C\]. Anchorage: 2021 IEEE International Conference on Image Processing (ICIP), 2021.

[9] [9] Zhang D, Zheng R. Trianglenet: edge prior augmented network for semantic segmentation through cross-task consistency \[J\]. arXiv: 2210.05152, 2022.

[10] [10] Liu H, Feng Y, Xu H, et al. MEA-Net: multilayer edge attention network for medical image segmentation \[J\]. Scientific Reports, 2022, 12(1): 7868.

[11] [11] Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation \[C\]. Munich: 15th European Conference on Computer Vision (ECCV), 2018.

[12] [12] Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network \[C\]. Honolulu: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[14] [14] Huang Z, Wang X, Huang L, et al. CCNet: criss-cross attention for semantic segmentation \[C\]. Seoul: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019.

[15] [15] Li X, Zhong Z, Wu J, et al. Expectation-maximization attention networks for semantic segmentation \[C\]. Seoul: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019.

[16] [16] Zhong Z, Lin Z Q, Bidart R, et al. Squeeze-and-attention networks for semantic segmentation \[C\]. Seattle: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

[17] [17] Takikawa T, Acuna D, Jampani V, et al. Gated-SCNN: gated shape CNNs for semantic segmentation \[C\]. Seoul: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019.

[18] [18] Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation \[J\]. arXiv: 1706.05587, 2017.

[19] [19] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation \[C\]. Munich: Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.

[20] [20] Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition \[J\]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 43(10): 3349-3364.

[21] [21] Kingma D P, Ba J. Adam: a method for stochastic optimization \[J\]. arXiv: 1412.6980, 2014.